Konstantinos Benidis

923 total citations · 1 hit paper
12 papers, 448 citations indexed

About

Konstantinos Benidis is a scholar working on Signal Processing, Management Science and Operations Research and Finance. According to data from OpenAlex, Konstantinos Benidis has authored 12 papers receiving a total of 448 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Signal Processing, 5 papers in Management Science and Operations Research and 4 papers in Finance. Recurrent topics in Konstantinos Benidis's work include Financial Markets and Investment Strategies (4 papers), Time Series Analysis and Forecasting (4 papers) and Stochastic processes and financial applications (4 papers). Konstantinos Benidis is often cited by papers focused on Financial Markets and Investment Strategies (4 papers), Time Series Analysis and Forecasting (4 papers) and Stochastic processes and financial applications (4 papers). Konstantinos Benidis collaborates with scholars based in Hong Kong, Germany and United States. Konstantinos Benidis's co-authors include Daniel P. Palomar, Tim Januschowski, Syama Sundar Rangapuram, Jan Gasthaus, David Salinas, Valentín Flunkert, Lorenzo Stella, Danielle C. Maddix, Michael Schneider and François-Xavier Aubet and has published in prestigious journals such as IEEE Transactions on Signal Processing, ACM Computing Surveys and Journal of Machine Learning Research.

In The Last Decade

Konstantinos Benidis

12 papers receiving 429 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

2022 150 citations Konstantinos Benidis, Syama Sundar Rangapuram et al. ACM Computing Surveys profile →

Peers

Countries citing papers authored by Konstantinos Benidis

Since Specialization

Citations

This map shows the geographic impact of Konstantinos Benidis's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Konstantinos Benidis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Konstantinos Benidis more than expected).

Fields of papers citing papers by Konstantinos Benidis

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Konstantinos Benidis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Konstantinos Benidis. The network helps show where Konstantinos Benidis may publish in the future.

Co-authorship network of co-authors of Konstantinos Benidis

This figure shows the co-authorship network connecting the top 25 collaborators of Konstantinos Benidis. A scholar is included among the top collaborators of Konstantinos Benidis based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Konstantinos Benidis. Konstantinos Benidis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

#	Work	Indexed citations
1	Deep Learning for Time Series Forecasting: Tutorial and Literature Survey breakdown → 2022 ·ACM Computing Surveys ·Konstantinos Benidis, Syama Sundar Rangapuram, Valentín Flunkert, (unknown), Danielle C. Maddix, (unknown), Jan Gasthaus, Michael Schneider, David Salinas, Lorenzo Stella, François-Xavier Aubet, Laurent Callot, Tim Januschowski	150
2	End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series 2021 ·CaltechAUTHORS (California Institute of Technology) ·Syama Sundar Rangapuram, (unknown), Konstantinos Benidis, Pedro Mercado, Jan Gasthaus, Tim Januschowski	21
3	GluonTS: Probabilistic and Neural Time Series Modeling in Python 2020 ·Journal of Machine Learning Research ·A. Alexandrov, Konstantinos Benidis, Michael Bohlke‐Schneider, Valentín Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Sundar Rangapuram, David Salinas, (unknown), Lorenzo Stella, Ali Caner Türkmen, (unknown)	71
4	Normalizing Kalman Filters for Multivariate Time Series Analysis 2020 ·Neural Information Processing Systems ·Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke‐Schneider, (unknown), Lorenzo Stella, (unknown), Patrick Gallinari, Tim Januschowski	44
5	Probabilistic Forecasting with Spline Quantile Function RNNs 2019 ·Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentín Flunkert, Tim Januschowski	43
6	Optimization Methods for Financial Index Tracking: From Theory to Practice 2018 ·Rare & Special e-Zone (The Hong Kong University of Science and Technology) ·Konstantinos Benidis, (unknown), Daniel P. Palomar	23
7	Optimization Methods for Financial Index Tracking: From Theory to Practice 2018 ·now publishers, Inc. eBooks ·Konstantinos Benidis, (unknown), (unknown)	2
8	Sparse Portfolios for High-Dimensional Financial Index Tracking 2017 ·SSRN Electronic Journal ·Konstantinos Benidis, (unknown), Daniel P. Palomar	3
9	Sparse Portfolios for High-Dimensional Financial Index Tracking 2017 ·IEEE Transactions on Signal Processing ·Konstantinos Benidis, (unknown), Daniel P. Palomar	53
10	Orthogonal sparse eigenvectors: A procrustes problem 2016 ·Rare & Special e-Zone (The Hong Kong University of Science and Technology) ·Konstantinos Benidis, Ying Sun, Prabhu Babu, Daniel P. Palomar	2
11	Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation 2016 ·IEEE Transactions on Signal Processing ·Konstantinos Benidis, Ying Sun, Prabhu Babu, Daniel P. Palomar	35
12	Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation 2016 ·SSRN Electronic Journal ·Konstantinos Benidis, Ying Sun, Prabhu Babu, Daniel P. Palomar	1

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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